Xupu Geng, Tian Li
Senior Engineer, Xiamen University, China
Title: Stylistic mixture of Monet and Chinese ink painting by deep learning
Biography
Biography: Xupu Geng, Tian Li
Abstract
Image style transfer is a classical problem in computer graphics and vision. As the palmy development of deep learning in recent years, Generative Adversarial Networks (GAN) and its variations like CycleGAN have been proposed to generate or transform images. Monet and Chinese Ink are two influential art styles in landscape painting. They have some likeness in impressionism, but concerning color and depth of focus, they are so different. Here we try to mix the two styles to create a new kind of artwork by CycleGAN. In fact the proposed method in this paper has many potential applications in artisitic creation.